5 papers · 1 filter
Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy
Jiashun Liu, Runze Liu, Xu Wan +3
Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses. Recent…
When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff
Runze Liu, Jiashun Liu, Xu Wan +2
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become a standard pipeline for Large Language Model (LLM) post-training. SFT is expected to provide a usefu…
Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
Xu Wan, Chao Yang, Cheng Yang +2
Safe Reinforcement Learning (RL) is crucial for achieving high performance while ensuring safety in real-world applications. However, the complex interplay of multiple uncertainty…
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
Xu Wan, Wei Wang, Wenyue Xu +3
Reinforcement Learning (RL)-based post-training has significantly advanced the complex reasoning capabilities of language models, fostering sophisticated self-reflection processes.…
SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance
Liyu Zhang, Haochi Wu, Xu Wan +3
Offline-to-online (O2O) reinforcement learning (RL) pre-trains models on offline data and refines policies through online fine-tuning. However, existing O2O RL algorithms typically…